A Multisource Domain Generalization Approach to Visual Attribute Detection
Attributes possess appealing properties and benefit many computer vision problems, such as object recognition, learning with humans in the loop, and image retrieval. Whereas the existing work mainly pursues utilizing attributes for various computer vision problems, we contend that the most basic problem—how to accurately and robustly detect attributes from images—has been left underexplored. Especially, the existing work rarely explicitly tackles the need that attribute detectors should generalize well across different categories, including those previously unseen. Noting that this is analogous to the objective of multisource domain generalization, if we treat each category as a domain, we provide a novel perspective to attribute detection and propose to gear the techniques in multisource domain generalization for the purpose of learning cross-category generalizable attribute detectors. We validate our understanding and approach with extensive experiments on four challenging datasets and two different problems.
This work was supported in part by NSF IIS-1566511. Chuang Gan was partially supported by the National Basic Research Program of China Grant 2011CBA00300, 2011CBA00301, the National Natural Science Foundation of China Grant 61033001, 61361136003. Tianbao Yang was partially supported by NSF IIS-1463988 and NSF IIS-1545995.